Online Course
Advanced Algorithmics and Graph Theory with Python
Institut Mines-Télécom via edX
Found in
Algorithms and Data Structures
- Provider edX
- Cost Free Online Course (Audit)
- Session Self Paced
- Language English
- Effort 2-4 hours a week
- Duration 6 weeks long
- Learn more about MOOCs
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Overview
Algorithmics and programming are fundamental skills for engineering students, data scientists and analysts, computer hobbyists or developers.
Learning how to program algorithms can be tedious if you aren’t given an opportunity to immediately practice what you learn. In this course, you won't just focus on theory or study a simple catalog of methods, procedures, and concepts. Instead, you’ll be given a challenge wherein you'll be asked to beat an algorithm we’ve written for you by coming up with your own clever solution.
To be specific, you’ll have to work out a route faster than your opponent through a maze while picking up objects.
Each week, you will learn new material to improve your artificial intelligence in order to beat your opponent. This structure means that as a learner, you’ll confront each abstract notion with a real-world problem.
We’ll go over data-structures, basic and advanced algorithms for graph theory, complexity/accuracy trade-offs, and even combinatorial game theory.
This course has received financial support from the Patrick and Lina Drahi Foundation.
Learning how to program algorithms can be tedious if you aren’t given an opportunity to immediately practice what you learn. In this course, you won't just focus on theory or study a simple catalog of methods, procedures, and concepts. Instead, you’ll be given a challenge wherein you'll be asked to beat an algorithm we’ve written for you by coming up with your own clever solution.
To be specific, you’ll have to work out a route faster than your opponent through a maze while picking up objects.
Each week, you will learn new material to improve your artificial intelligence in order to beat your opponent. This structure means that as a learner, you’ll confront each abstract notion with a real-world problem.
We’ll go over data-structures, basic and advanced algorithms for graph theory, complexity/accuracy trade-offs, and even combinatorial game theory.
This course has received financial support from the Patrick and Lina Drahi Foundation.
Syllabus
Week 1: Fundamentals of Graph Theory, Problem Solving, Good Programming Practices
Week 2: Graph Traversal, Routing, Queuing Structures
Week 3: Shortest Paths, Min-Heaps, Algorithmic Complexity
Week 4: NP-Completeness, Traveling Salesman Problem, Backtracking
Week 5: Heuristics, Greedy Approaches, Accuracy/Complexity tradeoff
Week 6: Combinatorial Game Theory, Winning Strategies
Week 2: Graph Traversal, Routing, Queuing Structures
Week 3: Shortest Paths, Min-Heaps, Algorithmic Complexity
Week 4: NP-Completeness, Traveling Salesman Problem, Backtracking
Week 5: Heuristics, Greedy Approaches, Accuracy/Complexity tradeoff
Week 6: Combinatorial Game Theory, Winning Strategies
Taught by
Vincent Gripon, Patrick Meyer, Nicolas Farrugia, Carlos Eduardo Rosar Kos Lassance and Ghouti Boukli Hacene
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